In semiconductor manufacturing processes, tool matching is used to ensure two or more of the same model of a semiconductor manufacturing tool (“tool”) produce similar results. The tool may be, for example, a semiconductor inspection tool (“inspection tool”). Tool matching may be particularly important for inspection tools. Variations between of inspection results from the same type of inspection tool can impact the root-cause analysis for the manufacturing process. For example, the actual semiconductor manufacturing drift may be confused with the variation in the inspection tools. This also may be important in other tools because other types of result variances, such as, for example, deposition thickness, chemical mechanical polishing uniformity, or ion implant angle, can impact root-cause analysis.
A semiconductor manufacturer usually performs an inspection tool matching exercise during periodic preventative maintenance (PM). Due to limited maintenance schedules, a field service engineer may only have one day to complete the PM, including a tool matching exercise. As inspection tools offer more powerful functionality and flexibility to semiconductor manufacturers, these inspection tools have become remarkably complex. Tens of thousands of parameters are tracked, and the collected data can be overwhelming for the field service engineer. It is extremely cumbersome for a field service engineer to review tens of thousands of parameters to determine the correct parameter(s) to adjust to match an inspection tool.
The complexity of inspection tools grows with each generation. Knowing the critical parameters that significantly impact tool performance helps engineers make decisions on which components or subsystems to improve in current and next generation inspection tools. Tool specifications need to be developed when inspection tools are manufactured. It is impractical to develop thousands of specifications for a tool. And it is very challenging to pick a handful of tool parameters out of over 10,000 measurable parameters that accurately characterize the performance of a tool. This small subset of parameters serves as the fingerprint of the tool and is sufficient to capture most of the tool variations, which makes it important to select the best tool parameters for the set.
In the current approach, a special wafer with known defect locations and sizes called a Defect Standard Wafer (“DSW wafer”) is created. An inspection tool uses the DSW wafer to compare the detected defect area with the actual defect area of the DSW wafer and can, for example, output a similarity score between 0 and 1. The similarity score, referred to as “Match Factor” or other tool performance measurements, measures the performance of an inspection tool at a system level. Systems engineers correlate one tool parameter at a time to Match Factor to examine which parameters can affect tool performance. Since a systems engineer cannot repeat this exercise for all tool parameters in a reasonable amount of time, the choice of parameters to investigate are purely based on the systems engineer's subjective discretion and experience. This can result in critical parameters being overlooked. Another drawback of a “one parameter at a time” approach is that it misses the interaction between the parameters. For example, an engineer may find two parameters have high impact on Match Factor. But these two parameters may be highly correlated. For example, one can be the surrogate of the other one. Thus, monitoring and creating specifications for both parameters may be redundant. Besides the performance measurement at a system level, this approach can also be applied to the a tool's sub-systems and function components.
Other statistical process control methods focus on seeking a single effective tool parameter to monitor tool excursions. However, this method may be ineffective for tools with a large volume of tool parameters. Furthermore, parameters may be inter-related or may not have enough of an effect to merit consideration, which may mean that the effective tool parameter can have an unintended effect or may not provide the largest potential effect. Therefore, what is needed is a technique that can quickly navigate a massive number of tool parameters and identify a few most important parameters that can affect tool performance.